def pagerank(
G, alpha=0.85, personalization=None, max_iter=100, tol=1.0e-5, nstart=None,
weight=None, dangling=None
):
"""
Find the PageRank score for every vertex in a graph. cuGraph computes an
approximation of the Pagerank eigenvector using the power method. The
number of iterations depends on the properties of the network itself; it
increases when the tolerance descreases and/or alpha increases toward the
limiting value of 1. The user is free to use default values or to provide
inputs for the initial guess, tolerance and maximum number of iterations.
Parameters
----------
G : cugraph.Graph or networkx.Graph
cuGraph graph descriptor, should contain the connectivity information
as an edge list.
The transposed adjacency list will be computed if not already present.
alpha : float, optional (default=0.85)
The damping factor alpha represents the probability to follow an
outgoing edge, standard value is 0.85.
Thus, 1.0-alpha is the probability to “teleport” to a random vertex.
Alpha should be greater than 0.0 and strictly lower than 1.0.
personalization : cudf.Dataframe, optional (default=None)
GPU Dataframe containing the personalization information.
personalization['vertex'] : cudf.Series
Subset of vertices of graph for personalization
personalization['values'] : cudf.Series
Personalization values for vertices
max_iter : int, optional (default=100)
The maximum number of iterations before an answer is returned. This can
be used to limit the execution time and do an early exit before the
solver reaches the convergence tolerance.
If this value is lower or equal to 0 cuGraph will use the default
value, which is 100.
tol : float, optional (default=1e-05)
Set the tolerance the approximation, this parameter should be a small
magnitude value.
The lower the tolerance the better the approximation. If this value is
0.0f, cuGraph will use the default value which is 1.0E-5.
Setting too small a tolerance can lead to non-convergence due to
numerical roundoff. Usually values between 0.01 and 0.00001 are
acceptable.
nstart : cudf.Dataframe, optional (default=None)
GPU Dataframe containing the initial guess for pagerank.
nstart['vertex'] : cudf.Series
Subset of vertices of graph for initial guess for pagerank values
nstart['values'] : cudf.Series
Pagerank values for vertices
weight: str, optional (default=None)
The attribute column to be used as edge weights if Graph is a NetworkX
Graph. This parameter is here for NetworkX compatibility and is ignored
in case of a cugraph.Graph
dangling : dict, optional (default=None)
This parameter is here for NetworkX compatibility and ignored
Returns
-------
PageRank : cudf.DataFrame
GPU data frame containing two cudf.Series of size V: the vertex
identifiers and the corresponding PageRank values.
df['vertex'] : cudf.Series
Contains the vertex identifiers
df['pagerank'] : cudf.Series
Contains the PageRank score
Examples
--------
>>> gdf = cudf.read_csv(datasets_path / 'karate.csv', delimiter=' ',
... dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(gdf, source='0', destination='1')
>>> pr = cugraph.pagerank(G, alpha = 0.85, max_iter = 500, tol = 1.0e-05)
"""
G, isNx = ensure_cugraph_obj_for_nx(G, weight)
if personalization is not None:
if not isinstance(personalization, cudf.DataFrame):
raise NotImplementedError(
"personalization other than a cudf dataframe "
"currently not supported"
)
if G.renumbered is True:
if len(G.renumber_map.implementation.col_names) > 1:
cols = personalization.columns[:-1].to_list()
else:
cols = 'vertex'
personalization = G.add_internal_vertex_id(
personalization, "vertex", cols
)
if nstart is not None:
if G.renumbered is True:
if len(G.renumber_map.implementation.col_names) > 1:
cols = nstart.columns[:-1].to_list()
else:
cols = 'vertex'
nstart = G.add_internal_vertex_id(
nstart, "vertex", cols
)
df = pagerank_wrapper.pagerank(
G, alpha, personalization, max_iter, tol, nstart
)
if G.renumbered:
df = G.unrenumber(df, "vertex")
if isNx is True:
return df_score_to_dictionary(df, 'pagerank')
else:
return df
def pagerank(G,
alpha=0.85,
personalization=None,
max_iter=100,
tol=1.0e-5,
nstart=None):
"""
Find the PageRank score for every vertex in a graph. cuGraph computes an
approximation of the Pagerank eigenvector using the power method. The
number of iterations depends on the properties of the network itself; it
increases when the tolerance descreases and/or alpha increases toward the
limiting value of 1. The user is free to use default values or to provide
inputs for the initial guess, tolerance and maximum number of iterations.
Parameters
----------
graph : cugraph.Graph
cuGraph graph descriptor, should contain the connectivity information
as an edge list (edge weights are not used for this algorithm).
The transposed adjacency list will be computed if not already present.
alpha : float
The damping factor alpha represents the probability to follow an
outgoing edge, standard value is 0.85.
Thus, 1.0-alpha is the probability to “teleport” to a random vertex.
Alpha should be greater than 0.0 and strictly lower than 1.0.
personalization : cudf.Dataframe
GPU Dataframe containing the personalization information.
personalization['vertex'] : cudf.Series
Subset of vertices of graph for personalization
personalization['values'] : cudf.Series
Personalization values for vertices
max_iter : int
The maximum number of iterations before an answer is returned. This can
be used to limit the execution time and do an early exit before the
solver reaches the convergence tolerance.
If this value is lower or equal to 0 cuGraph will use the default
value, which is 100.
tolerance : float
Set the tolerance the approximation, this parameter should be a small
magnitude value.
The lower the tolerance the better the approximation. If this value is
0.0f, cuGraph will use the default value which is 1.0E-5.
Setting too small a tolerance can lead to non-convergence due to
numerical roundoff. Usually values between 0.01 and 0.00001 are
acceptable.
nstart : cudf.Dataframe
GPU Dataframe containing the initial guess for pagerank.
nstart['vertex'] : cudf.Series
Subset of vertices of graph for initial guess for pagerank values
nstart['values'] : cudf.Series
Pagerank values for vertices
Returns
-------
PageRank : cudf.DataFrame
GPU data frame containing two cudf.Series of size V: the vertex
identifiers and the corresponding PageRank values.
df['vertex'] : cudf.Series
Contains the vertex identifiers
df['pagerank'] : cudf.Series
Contains the PageRank score
Examples
--------
>>> gdf = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>> dtype=['int32', 'int32', 'float32'], header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(gdf, source='0', destination='1')
>>> pr = cugraph.pagerank(G, alpha = 0.85, max_iter = 500, tol = 1.0e-05)
"""
if personalization is not None:
null_check(personalization["vertex"])
null_check(personalization["values"])
if G.renumbered is True:
personalization = G.add_internal_vertex_id(personalization,
"vertex", "vertex")
if nstart is not None:
if G.renumbered is True:
nstart = G.add_internal_vertex_id(nstart, "vertex", "vertex")
df = pagerank_wrapper.pagerank(G, alpha, personalization, max_iter, tol,
nstart)
if G.renumbered:
df = G.unrenumber(df, "vertex")
return df